{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JwAn1Jr86udO"
   },
   "source": [
    "# Introduction to Logistic Regression \n",
    "\n",
    "\n",
    "\n",
    "## Learning objectives\n",
    "\n",
    "1. Create Seaborn plots for Exploratory Data Analysis. \n",
    "2. Train a Logistic Regression Model using Scikit-Learn.\n",
    "\n",
    "\n",
    "## Introduction \n",
    "\n",
    "This lab is an introduction to logistic regression using Python and Scikit-Learn.  This lab serves as a foundation for more complex algorithms and machine learning models that you will encounter in the course. In this lab, you will use a synthetic advertising data set, indicating whether or not a particular internet user clicked on an Advertisement on a company website. You will try to create a model that will predict whether or not they will click on an ad based off the features of that user.  \n",
    "\n",
    "\n",
    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/intro_logistic_regression.ipynb) -- try to complete that notebook first before reviewing this solution notebook. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8z-hq2Co6udQ"
   },
   "outputs": [],
   "source": [
    "# You can use any Python source file as a module by executing an import statement in some other Python source file.\n",
    "# The import statement combines two operations; it searches for the named module, then it binds the\n",
    "# results of that search to a name in the local scope.\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Import matplotlib to visualize the model\n",
    "import matplotlib.pyplot as plt\n",
    "# Seaborn is a Python data visualization library based on matplotlib\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XuU2AljQ6udT"
   },
   "source": [
    "###  Load the Dataset\n",
    "\n",
    "You will use a synthetic [advertising](https://www.kaggle.com/fayomi/advertising) dataset.  This data set contains the following features:\n",
    "\n",
    "* 'Daily Time Spent on Site': consumer time on site in minutes\n",
    "* 'Age': customer age in years\n",
    "* 'Area Income': Avg. Income of geographical area of consumer\n",
    "* 'Daily Internet Usage': Avg. minutes a day consumer is on the internet\n",
    "* 'Ad Topic Line': Headline of the advertisement\n",
    "* 'City': City of consumer\n",
    "* 'Male': Whether or not consumer was male\n",
    "* 'Country': Country of consumer\n",
    "* 'Timestamp': Time at which consumer clicked on Ad or closed window\n",
    "* 'Clicked on Ad': 0 or 1 indicated clicking on Ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MXAy-rgp6udU"
   },
   "outputs": [],
   "source": [
    "# TODO 1: Read in the advertising.csv file and set it to a data frame called ad_data.\n",
    "ad_data = pd.read_csv('../advertising.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lqtY61dE6udW"
   },
   "source": [
    "**Check the head of ad_data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "wS9c7okn6udX",
    "outputId": "3be715b4-850a-4ece-f565-9d9ebcc8ad52"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Ad Topic Line</th>\n",
       "      <th>City</th>\n",
       "      <th>Male</th>\n",
       "      <th>Country</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>0</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>2016-03-27 00:53:11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>1</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>2016-04-04 01:39:02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>0</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>2016-03-13 20:35:42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>54806.18</td>\n",
       "      <td>245.89</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2016-01-10 02:31:19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>73889.99</td>\n",
       "      <td>225.58</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "      <td>South Manuel</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>2016-06-03 03:36:18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
       "0                     68.95   35     61833.90                256.09   \n",
       "1                     80.23   31     68441.85                193.77   \n",
       "2                     69.47   26     59785.94                236.50   \n",
       "3                     74.15   29     54806.18                245.89   \n",
       "4                     68.37   35     73889.99                225.58   \n",
       "\n",
       "                           Ad Topic Line            City  Male     Country  \\\n",
       "0     Cloned 5thgeneration orchestration     Wrightburgh     0     Tunisia   \n",
       "1     Monitored national standardization       West Jodi     1       Nauru   \n",
       "2       Organic bottom-line service-desk        Davidton     0  San Marino   \n",
       "3  Triple-buffered reciprocal time-frame  West Terrifurt     1       Italy   \n",
       "4          Robust logistical utilization    South Manuel     0     Iceland   \n",
       "\n",
       "             Timestamp  Clicked on Ad  \n",
       "0  2016-03-27 00:53:11              0  \n",
       "1  2016-04-04 01:39:02              0  \n",
       "2  2016-03-13 20:35:42              0  \n",
       "3  2016-01-10 02:31:19              0  \n",
       "4  2016-06-03 03:36:18              0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first five rows of DataFrame ad_data.\n",
    "ad_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WWCSKrwq6udZ"
   },
   "source": [
    "**Use info and describe() on ad_data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "0LTHowWE6uda",
    "outputId": "b22829e2-f0fa-49db-89c9-2e8c0ae23d7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Daily Time Spent on Site  1000 non-null   float64\n",
      " 1   Age                       1000 non-null   int64  \n",
      " 2   Area Income               1000 non-null   float64\n",
      " 3   Daily Internet Usage      1000 non-null   float64\n",
      " 4   Ad Topic Line             1000 non-null   object \n",
      " 5   City                      1000 non-null   object \n",
      " 6   Male                      1000 non-null   int64  \n",
      " 7   Country                   1000 non-null   object \n",
      " 8   Timestamp                 1000 non-null   object \n",
      " 9   Clicked on Ad             1000 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(4)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get a concise summary of DataFrame ad_data.\n",
    "ad_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "7exNAo7r6udc",
    "outputId": "b6777af0-bfff-4ac9-89ff-8f324b133fe6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Male</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>65.000200</td>\n",
       "      <td>36.009000</td>\n",
       "      <td>55000.000080</td>\n",
       "      <td>180.000100</td>\n",
       "      <td>0.481000</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.853615</td>\n",
       "      <td>8.785562</td>\n",
       "      <td>13414.634022</td>\n",
       "      <td>43.902339</td>\n",
       "      <td>0.499889</td>\n",
       "      <td>0.50025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>13996.500000</td>\n",
       "      <td>104.780000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.360000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>47031.802500</td>\n",
       "      <td>138.830000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>68.215000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>57012.300000</td>\n",
       "      <td>183.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.547500</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>65470.635000</td>\n",
       "      <td>218.792500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>91.430000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>79484.800000</td>\n",
       "      <td>269.960000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Daily Time Spent on Site          Age   Area Income  \\\n",
       "count               1000.000000  1000.000000   1000.000000   \n",
       "mean                  65.000200    36.009000  55000.000080   \n",
       "std                   15.853615     8.785562  13414.634022   \n",
       "min                   32.600000    19.000000  13996.500000   \n",
       "25%                   51.360000    29.000000  47031.802500   \n",
       "50%                   68.215000    35.000000  57012.300000   \n",
       "75%                   78.547500    42.000000  65470.635000   \n",
       "max                   91.430000    61.000000  79484.800000   \n",
       "\n",
       "       Daily Internet Usage         Male  Clicked on Ad  \n",
       "count           1000.000000  1000.000000     1000.00000  \n",
       "mean             180.000100     0.481000        0.50000  \n",
       "std               43.902339     0.499889        0.50025  \n",
       "min              104.780000     0.000000        0.00000  \n",
       "25%              138.830000     0.000000        0.00000  \n",
       "50%              183.130000     0.000000        0.50000  \n",
       "75%              218.792500     1.000000        1.00000  \n",
       "max              269.960000     1.000000        1.00000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the statistical summary of the DataFrame ad_data.\n",
    "ad_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for any null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daily Time Spent on Site    0\n",
       "Age                         0\n",
       "Area Income                 0\n",
       "Daily Internet Usage        0\n",
       "Ad Topic Line               0\n",
       "City                        0\n",
       "Male                        0\n",
       "Country                     0\n",
       "Timestamp                   0\n",
       "Clicked on Ad               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The isnull() method is used to check and manage NULL values in a data frame.\n",
    "ad_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tNDtwGLU6ude"
   },
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n",
    "Let's use seaborn to explore the data!  Try recreating the plots shown below!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO 1:  **Create a histogram of the Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "_cZQWiIa6udf",
    "outputId": "5064a339-ccdb-46c6-ea47-d5705185eb49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 1\n",
    "# Seaborn is a Python data visualization library based on matplotlib.\n",
    "# Set the aesthetic style of the plots.\n",
    "sns.set_style('whitegrid')\n",
    "ad_data['Age'].hist(bins=30)\n",
    "plt.xlabel('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gc8IkCJV6udh"
   },
   "source": [
    "TODO 1:  **Create a jointplot showing Area Income versus Age.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "colab_type": "code",
    "id": "uoI729Bk6udh",
    "outputId": "3907e402-448c-4568-cb7b-8a387ce61cf2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f9391624d68>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 1\n",
    "# Seaborn’s jointplot displays a relationship between 2 variables (bivariate) as well as\n",
    "# 1D profiles (univariate) in the margins.\n",
    "sns.jointplot(x='Age',y='Area Income',data=ad_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y_eq6CJs6udj"
   },
   "source": [
    "TODO 2:  **Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "colab_type": "code",
    "id": "0w695Wda6udk",
    "outputId": "956e69b1-a108-48d3-d0a0-20424369c65a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 2\n",
    "sns.jointplot(x='Age',y='Daily Time Spent on Site',data=ad_data,color='red',kind='kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tDM8yyQR6udm"
   },
   "source": [
    "TODO 1: **Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "colab_type": "code",
    "id": "TygXpR2Y6udm",
    "outputId": "e16c0cba-f120-4191-cb2e-5581168d0972"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f939100da90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 1\n",
    "sns.jointplot(x='Daily Time Spent on Site',y='Daily Internet Usage',data=ad_data,color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AHeR3th76udr"
   },
   "source": [
    "# Logistic Regression\n",
    "\n",
    "Logistic regression is a supervised machine learning process.  It is similar to linear regression, but rather than predict a continuous value, you try to  estimate probabilities by using a logistic function.  Note that even though it has regression in the name, it is for classification.\n",
    "While linear regression is acceptable for estimating values, logistic regression is best for predicting the class of an observation\n",
    "\n",
    "Now it's time to do a train test split, and train our model!  You'll have the freedom here to choose columns that you want to train on!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3Z2BXtOt6uds"
   },
   "outputs": [],
   "source": [
    "# `train_test_split` is a function in Sklearn model selection for splitting data arrays into two subsets:\n",
    "# for training data and for testing data.\n",
    "# With this function, you don't need to divide the dataset manually.\n",
    "# By default, Sklearn `train_test_split` will make random partitions for the two subsets.\n",
    "# However, you can also specify a random state for the operation.\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's define the features and label.  Briefly, feature is input; label is output. This applies to both classification and regression problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kjlnug3W6udt"
   },
   "outputs": [],
   "source": [
    "X = ad_data[['Daily Time Spent on Site', 'Age', 'Area Income','Daily Internet Usage', 'Male']]\n",
    "y = ad_data['Clicked on Ad']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO 2: **Split the data into training set and testing set using train_test_split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WtK2DMqd6udv"
   },
   "outputs": [],
   "source": [
    "# TODO 2\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hh-cXKCb6udy"
   },
   "source": [
    "**Train and fit a logistic regression model on the training set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0PnZOrge6udy"
   },
   "outputs": [],
   "source": [
    "# Logistic Regression is one of the most simple and commonly used Machine Learning algorithms for two-class classification.\n",
    "# It is easy to implement and can be used as the baseline for any binary classification problem.\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "XhqwY1ZG6ud0",
    "outputId": "6688f1e4-4ade-4b8c-d0e2-90a9fb662cbe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QV3-c2qF6ud2"
   },
   "source": [
    "## Predictions and Evaluations\n",
    "**Now predict values for the testing data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UA72s_Vt6ud2"
   },
   "outputs": [],
   "source": [
    "# Use predict() function to predict values for the testing data.\n",
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vYpC2Yc46ud4"
   },
   "source": [
    "**Create a classification report for the model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dFD-TA186ud5"
   },
   "outputs": [],
   "source": [
    "# The classification_report function builds a text report showing the main classification metrics.\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "NrQ6CswF6ud8",
    "outputId": "6725c756-4394-4b14-821e-205c6775e8fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.96      0.91       162\n",
      "           1       0.96      0.85      0.90       168\n",
      "\n",
      "    accuracy                           0.91       330\n",
      "   macro avg       0.91      0.91      0.91       330\n",
      "weighted avg       0.91      0.91      0.91       330\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1-ruS5jy6ud9"
   },
   "source": [
    "Copyright 2022 Google Inc.  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
    "http://www.apache.org/licenses/LICENSE-2.0\n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Conchita_Logistic_Regression.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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